arXiv subjects

Mophat Okinyi

Publications and source records attributed to Mophat Okinyi.

4 recordsLinked to original sources

arXiv2026

Hidden Underbelly of the Silicon Valley: Algorithmic Exploitation and Health in Data Work Value Chains

With robots expected to replace humans in some professions, AI presents a new development prospect through the provisions of data work. For over a decade, large Silicon Valley technology firms have been relying on outsourcing of data work via a host of intermediary suppliers and labour platforms to different parts of the globe often the Global South region, such as East Africa. Much of this work is often shrouded in secrecy as firms rarely reveal the extent of their value chains. This results in the poor and marginalised forming the hidden underbelly of the Silicon Valley, training some of their most advanced machines in adverse working conditions. Drawing upon the survey of workers in Kenya, a major hub for data work in Africa, the paper highlights the physical and psychological impacts on workers. Survey data is complemented with in-depth interviews and auto-ethnographic account of two ex-data workers-turned activists who worked for a large data enrichment firms in Kenya. Overall, the study highlights serious mental and physical health issues experienced humans behind the making of AI systems.

cs.CY↗

arXiv2026

How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI

Large-scale data has fuelled the success of frontier artificial intelligence (AI) models over the past decade. This expansion has relied on sustained efforts by large technology corporations to aggregate and curate internet-scale datasets. In this work, we examine the environmental, social, and economic costs of large-scale data in AI through a sustainability lens. We argue that the field is shifting from building models from data to actively creating data for building models. We characterise this transition as hyper-datafication, which marks a critical juncture for the future of frontier AI and its societal impacts. To quantify and contextualise data-related costs, we analyse approximately 550,000 datasets from the Hugging Face Hub, focusing on dataset growth, storage-related energy consumption and carbon footprint, and societal representation using language data. We complement this analysis with qualitative responses from data workers in Kenya to examine the labour involved, including direct employment by big tech corporations and exposure to graphic content. We further draw on external data sources to substantiate our findings by illustrating the global disparity in data centre infrastructure. Our analyses reveal that hyper-datafication drives substantial and growing environmental costs while systematically redistributing labour risks and representational harms toward the Global South. Thus, we propose Data PROOFS recommendations spanning provenance, resource awareness, ownership, openness, frugality, and standards to mitigate these costs. Our work aims to make visible the often-overlooked costs of data that underpin frontier AI and to stimulate broader debate within the research community and beyond.

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arXiv2026

Auditing African Content Moderators' Working Conditions by Using the European General Data Protection Regulation (GDPR)

In this article, we audit the working conditions of content moderators in Kenya and Nigeria employed by business process outsourcing (BPO) companies by using the European General Data Protection Regulation (GDPR). We demonstrate its extraterritorial scope for gaining access to elements such as employment contracts and NDAs that have never been provided to the workers concerned. The results of this approach provide legally grounded evidence of the structural disadvantages faced by content moderators in the Global South, whose exploitative working conditions violate workers' rights. Our work also highlights the benefits of legislation aimed at protecting individuals' data rights as a counterweight to the tech industry's discourse of exceptionalism, which obscures its dependence on BPOs to externalise labour costs and accountability, whilst claiming that its products, business models, and methods of resource extraction are unprecedented and fall outside any existing legal framework.

cs.CY↗

arXiv2026

Mapping Data Labour Supply Chain in Africa in an Era of Digital Apartheid: a Struggle for Recognition

Content moderation and data annotation work has shifted to the Global South, particularly Africa, where workers at business process outsourcing (BPO) companies operate under precarity to serve Global North needs. We address the invisibility of this data labour supply chain and the underdocumented working conditions of its workforce. Drawing on a participatory collaboration between academics, an NGO, and a union, we conducted desk research and deployed a questionnaire (n=81) attuned to unions' organising goals. Our findings show that data labour spans 43 out of 55 African countries, involving 17 major firms serving predominantly North-American and European clients, with workers employed on short-term contracts, under psychological stress and economic instability - conditions that obscure the competences, i.e. adaptability and resilience, that their work demands. We contribute the first comprehensive map of Africa's data labour industry and demonstrate a methodology that centers workers' collective actions in documenting their conditions, drawing on Honneth's "struggle for recognition" to capture workers' demands for professional and social acknowledgement.

cs.CY↗